analysis

library(babynamesIL)
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
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#> ✔ tidyr   1.2.0     ✔ stringr 1.4.0
#> ✔ readr   2.1.2     ✔ forcats 0.5.1
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#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
library(tgstat)
theme_set(theme_classic())

Israeli baby names

Distribution of names

We will start by looking at the distribution total number of babies for each name:

babynamesIL_totals %>%
  mutate(sector = factor(sector, levels = c("Jewish", "Muslim", "Christian", "Druze", "Other"))) %>% 
  ggplot(aes(x = total, color = sex)) +
  ggsci::scale_color_aaas() +
  geom_density() +
  scale_x_log10() +
  facet_grid(.~sector) + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

Note that the x axis is in log scale.

Top names

Top 20 names in each sex and sector:

babynamesIL_totals %>%
  mutate(sector = factor(sector, levels = c("Jewish", "Muslim", "Christian", "Druze", "Other"))) %>%
  group_by(sector, sex) %>%
  slice_max(order_by = total, n = 20) %>%
  arrange(sector, sex, desc(total)) %>%  
  mutate(name = forcats::fct_inorder(name)) %>% 
  ggplot(aes(x = name, y = total)) +
    geom_col() +
    facet_wrap(sector ~ sex, scales = "free", ncol = 2) +
    ylab("total #") + 
    xlab("") + 
    theme(axis.text.x = element_text(angle = 90, hjust = 1))

Clustering

We will then create a matrix of the names and their frequencies over time for Jewish female babies.

names_mat <- babynamesIL %>%
        filter(sector == "Jewish", sex == "F") %>%
        select(year, name, prop) %>%
        spread(year, prop, fill = 0) %>%
        column_to_rownames("name") %>%
        as.matrix()
dim(names_mat)
#> [1] 1478   74

Normalize each name:

mat_norm <- names_mat / rowSums(names_mat)

Select only names with at least 500 babies:

mat_norm_f <- mat_norm[babynamesIL_totals %>% filter(sector == "Jewish", sex == "F") %>% filter(total >= 500) %>% pull(name), ]
dim(mat_norm_f)
#> [1] 485  74

Cluster:

hc <- tgs_cor(t(mat_norm_f)) %>% tgs_dist() %>% hclust(method = "ward.D2")

Reorder the clustering by year:

hc <- as.hclust(reorder(
  as.dendrogram(hc),
  apply(mat_norm_f, 1, which.max),
  agglo.FUN = mean
))

Plot the matrix:

text_mat <- babynamesIL %>%
  filter(sector == "Jewish", sex == "F") %>%
  tidyr::complete(sector, year, sex, name, fill = list(n = NA)) %>%
  mutate(text = paste(name, paste0("year: ", year), paste0("n: ", n), sep = "\n")) %>%
  select(year, name, text) %>%
  spread(year, text) %>%
  column_to_rownames("name") %>%
  as.matrix()
plotly::plot_ly(z = mat_norm_f[hc$order, ], y = rownames(mat_norm_f)[hc$order], x = colnames(mat_norm_f), type = "heatmap", colors = colorRampPalette(c("white", "blue", "red", "yellow"))(1000), hoverinfo = "text", text = text_mat[hc$order, ]) %>%
  plotly::layout(yaxis = list(title = ""), xaxis = list(title = "Year"))

We will wrap it all in a function:

cluster_names <- function(sector, sex, min_total = 500, colors = colorRampPalette(c
  ("white", "blue", "red", "yellow"))(1000)){
  names_mat <- babynamesIL %>%
    filter(sector == !!sector, sex == !!sex) %>%
    select(year, name, prop) %>%
    spread(year, prop, fill = 0) %>%
    column_to_rownames("name") %>%
    as.matrix()
  text_mat <- babynamesIL %>%
    filter(sector == !!sector, sex == !!sex) %>%
    tidyr::complete(sector, year, sex, name, fill = list(n = NA)) %>% 
    mutate(text = paste(name, paste0("year: ", year), paste0("n: ", n), sep = "\n")) %>% 
    select(year, name, text) %>%
    spread(year, text) %>%
    column_to_rownames("name") %>%
    as.matrix()
  mat_norm <- names_mat / rowSums(names_mat)
  mat_norm_f <- mat_norm[babynamesIL_totals %>%
    filter(sector == !!sector, sex == !!sex) %>%
    filter(total >= min_total) %>%
    pull(name), ]
  text_mat <- text_mat[rownames(mat_norm_f), colnames(mat_norm_f)]
  hc <- tgs_cor(t(mat_norm_f)) %>%
    tgs_dist() %>%
    hclust(method = "ward.D2")
  hc <- as.hclust(reorder(
    as.dendrogram(hc),
    apply(mat_norm_f, 1, which.max),
    agglo.FUN = mean
  ))
  plotly::plot_ly(z = mat_norm_f[hc$order, ], y = rownames(mat_norm_f)[hc$order], x = colnames(mat_norm_f), type = "heatmap", colors = colors, hoverinfo = "text", text = text_mat[hc$order, ]) %>%
    plotly::layout(yaxis = list(title = ""), xaxis = list(title = "Year"))
}

We can now plot also the Male names:

cluster_names("Jewish", "M")

Or other sectors:

cluster_names("Muslim", "M")

cluster_names("Muslim", "F")

cluster_names("Christian", "M", 50)

cluster_names("Christian", "F", 50)

cluster_names("Druze", "M", 50)

cluster_names("Druze", "F", 50)

cluster_names("Other", "M", 50)

cluster_names("Other", "F", 50)